analyze_data_insights

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It details parameter behavior (e.g., date_range format constraint) and notes return type as JSON. However, it does not disclose whether the tool is read-only, any potential side effects, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for purpose, Args, Returns, and Examples. While comprehensive, it could be slightly more concise; but the front-loaded purpose and clear organization justify a high score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, an output schema (exists), and no annotations, the description covers parameter details, constraints, and examples. It lacks information on error scenarios and output structure beyond 'JSON format', but the presence of an output schema reduces the need for full return value description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the Args section thoroughly explains each parameter: enum values for insight_type with meanings, topic as optional, date_range with format example, min_frequency default, top_n default. This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states '统一数据洞察分析工具 - 整合多种数据分析模式', indicating a unified tool for multiple data analysis modes. It lists three specific insight types, distinguishing it from sibling tools like analyze_sentiment or analyze_topic_trend which are more specialized.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit insight_type options and examples showing parameter usage for different scenarios. However, it lacks explicit guidance on when not to use this tool or direct comparisons to alternatives, leaving usage context somewhat implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.